complex environment evolution
TRANSCRIPT
Complex Environment Evolution
Challenges with Semantic Service Infrastructures
- Andrej Eisfeld- Achim P. Karduck- David McMeekin IEEE DEST: 18 - 20 June 2012
Complex Environment Evolution2
2
Structure
Background
Semantic Agents
Evaluation
Conclusion
Complex Environment Evolution3
3
Background Semantic Agents Use Case Conclusion
Smart Camp
Aim: Reduce energy consumption in camps
Example:
Energy costs: 2.000.000 AUD / year
25% savings potential
Main Smart Camp System components:
Smart Home Controller (SHC)
Smart Camp Management Unit (SCMU)
Complex Environment Evolution4
4
Background Semantic Agents Use Case Conclusion
Problem I
Continuing Change
“E-type systems must be continually adapted or they become progressively less satisfactory”
Continuing Growth
“The functional content of E-type systems must be continually increased to maintain user satisfaction over
their lifetime”
Complex Environment Evolution5
5
Background Semantic Agents Use Case Conclusion
Problem II
Multiple software systems in service infrastructure
Evolution more difficult due to dependencies
Complex Environment Evolution6
6
Background Semantic Agents Use Case Conclusion
Semantic Service Approaches
Approach Loose Coupling
WSDL2.0 + SAWSDL x
HTML + SA-REST
HTML + hRESTs + MicroWSMO
EXPRESS
ReLL
JSON-LD
Comparison of multiple Semantic Service aproaches
Complex Environment Evolution7
7
Background Semantic Agents Use Case Conclusion
Linked Data II
JSON-LD is resource orientated
Linked Resources Graph (LRG):
Complex Environment Evolution8
8
Background Semantic Agents Use Case Conclusion
Idea I : LRG Ontology
Resource Discovery
Resource Composition
Resource Invocation
Complex Environment Evolution9
9
Background Semantic Agents Use Case Conclusion
Idea II : Ontology Paths
Permitted Ontology Path (POP)
Not Permitted Ontology Path (NPOP)
POP + NPOP → Restrictions for LRG traversal
Complex Environment Evolution10
10
Background Semantic Agents Use Case Conclusion
Semantic Handler
Semantic Request Handler
Resorce Discovery + Composition + Invocation
Semantic Response Handler
Data Discovery + Dynamic Code Reuse
Complex Environment Evolution11
11
Background Semantic Agents Use Case Conclusion
Agent Communication
1) Define Goal
2) Traverse LRG
3) Retrieve Response
4) Process Response
Complex Environment Evolution12
12
Background Semantic Agents Use Case Conclusion
A Semantic Camp
SCMU and SHCs as Semantic Agents
Flexibility for Resource's location and content
Functionality enrichment without recompilation
Complex Environment Evolution13
13
Background Semantic Agents Use Case Conclusion
Setting
Linked Resources GraphSmart Camp Ontology
Complex Environment Evolution14
14
Background Semantic Agents Use Case Conclusion
Resource Discovery
Smart Camp Ontology Linked Resources Graph
Complex Environment Evolution15
15
Background Semantic Agents Use Case Conclusion
Representations
{
"@context":{
"onto":"http://www.smartcamp.org/onto"
"door":"onto#DoorSensor"
"value":"onto#sensorValue"
},
"@type":"door",
"value":true
}
{
"@context":{
"onto":"http://www.smartcamp.org/onto"
"motion":"onto#MotionSensor"
"value":"onto#sensorValue"
},
"@type":"motion",
"valueZ":false
}
Complex Environment Evolution16
16
Background Semantic Agents Use Case Conclusion
Composed Representation
{
"@context":{
"motion":"http://www.smartcamp.org/ontology#MotionSensor",
"door":"http://www.smartcamp.org/ontology#DoorSensor",
"value":"http://www.smartcamp.org/ontology#sensorValue"
},
"@type":"http://www.smartcamp.org/ontology#Sensor",
"motion":{
"value":false
},
"door":{
"value":true
}
}
Complex Environment Evolution17
17
Background Semantic Agents Use Case Conclusion
What if ...
● Requirements change → new sensors● Requirements change → obsolete sensors
Complex Environment Evolution18
18
Background Semantic Agents Use Case Conclusion
Summary
Chosen technologies: JSON-LD + OWL
Model of a Semantic Agent
Higher evolvability in evolution scenario
Ontology Evolution may reduce assessed evolvability
Complex Environment Evolution19
19
Background Semantic Agents Use Case Conclusion
Outlook
Implementation
Research Ontology Evolution & Versioning
Service Discovery in a Smart City
Complex Environment Evolution21
21
References● M. Lehman. On understanding laws, evolution, and conservation in the large-
program life cycle. Journal of Systems and Software, 1:213–221, 1980● H. P. Breivold, I. Crnkovic, R. Land, and S. Larsson. Using dependency model
to support software architecture evolution. In Automated Software Engineering - Workshops, 2008. ASE Workshops 2008. 23rd IEEE/ACM International Conference on, pages 82–91, 2008.
● P.V.D. Laar and T. Punter. Views on Evolvability of Embedded Systems. Springer, 2010.
● Ora Lassila, Tim Berners-Lee, James A. Hendler. The semantic web. Scientific American, 284(5):34–43, 2001.
● http://www.cs.helsinki.fi/research/roosa/images/serious-logo-final.jpg● http://applicanttracking.files.wordpress.com/2010/06/evolution.jpg● http://informatique.umons.ac.be/genlog/images/wordle.jpg● http://www.johnbendever.com/wp-content/uploads/question.jpg
Complex Environment Evolution22
22
DNS Service Discovery
Different types of resource records
PTR: Defines references to other domains
SRV: Defines a service location
TXT: Used to add meta-data
------------------------------------------------------------------
General usage:
serviceType PTR serviceInstance
serviceInstance SRV serviceLocation
TXT serviceMetaData